DS_Proj_1_0_copy.ipynb

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        "<a href=\"https://colab.research.google.com/github/adamsyed33/Grad-5100-Final-Project/blob/main/DS_Proj_1_0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Import Module\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from pylab import *\n",
        "import matplotlib.pyplot as plt # for plotting\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import  MinMaxScaler\n",
        "# permutation testing\n",
        "from sklearn.inspection import permutation_importance\n",
        "from imblearn.over_sampling import SMOTE\n"
      ],
      "metadata": {
        "id": "GyBNzYPpqvIL"
      },
      "execution_count": null,
      "outputs": []
    },
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        "id": "6xRunD7ypW24",
        "outputId": "18ec5991-d87a-421d-8e2d-876c328ab124"
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# mount google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing the data"
      ],
      "metadata": {
        "id": "zo8mz8XNq_FU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/train.csv')"
      ],
      "metadata": {
        "id": "3zJBG6BqqzM1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA"
      ],
      "metadata": {
        "id": "RFjwjE_DrCXU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2660 entries, 0 to 2659\n",
            "Data columns (total 78 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   ID             2660 non-null   int64  \n",
            " 1   TR_1_EventInd  73 non-null     float64\n",
            " 2   TR_2_EventInd  61 non-null     float64\n",
            " 3   TR_3_EventInd  97 non-null     float64\n",
            " 4   feature_10_A   2212 non-null   float64\n",
            " 5   feature_10_B   1843 non-null   float64\n",
            " 6   feature_10_F   2212 non-null   float64\n",
            " 7   feature_10_G   2212 non-null   float64\n",
            " 8   feature_1_A    1844 non-null   float64\n",
            " 9   feature_1_B    1844 non-null   float64\n",
            " 10  feature_1_C    1840 non-null   float64\n",
            " 11  feature_1_D    1840 non-null   float64\n",
            " 12  feature_1_E    1742 non-null   float64\n",
            " 13  feature_1_F    1844 non-null   float64\n",
            " 14  feature_1_G    1844 non-null   float64\n",
            " 15  feature_2_A    1844 non-null   float64\n",
            " 16  feature_2_B    1844 non-null   float64\n",
            " 17  feature_2_C    1840 non-null   float64\n",
            " 18  feature_2_D    1840 non-null   float64\n",
            " 19  feature_2_E    1742 non-null   float64\n",
            " 20  feature_2_F    1844 non-null   float64\n",
            " 21  feature_2_G    1844 non-null   float64\n",
            " 22  feature_3_A    1844 non-null   float64\n",
            " 23  feature_3_B    1844 non-null   float64\n",
            " 24  feature_3_C    1840 non-null   float64\n",
            " 25  feature_3_D    1840 non-null   float64\n",
            " 26  feature_3_E    1742 non-null   float64\n",
            " 27  feature_3_F    1844 non-null   float64\n",
            " 28  feature_3_G    1844 non-null   float64\n",
            " 29  feature_4_A    1915 non-null   float64\n",
            " 30  feature_4_B    1912 non-null   float64\n",
            " 31  feature_4_C    1884 non-null   float64\n",
            " 32  feature_4_E    1513 non-null   float64\n",
            " 33  feature_4_F    1915 non-null   float64\n",
            " 34  feature_4_G    1915 non-null   float64\n",
            " 35  feature_5_A    1843 non-null   float64\n",
            " 36  feature_5_B    1843 non-null   float64\n",
            " 37  feature_5_C    1839 non-null   float64\n",
            " 38  feature_5_D    1839 non-null   float64\n",
            " 39  feature_5_E    1741 non-null   float64\n",
            " 40  feature_5_F    1843 non-null   float64\n",
            " 41  feature_5_G    1843 non-null   float64\n",
            " 42  feature_6_A    1844 non-null   float64\n",
            " 43  feature_6_B    1844 non-null   float64\n",
            " 44  feature_6_C    1840 non-null   float64\n",
            " 45  feature_6_D    1840 non-null   float64\n",
            " 46  feature_6_E    1742 non-null   float64\n",
            " 47  feature_6_F    1844 non-null   float64\n",
            " 48  feature_6_G    1844 non-null   float64\n",
            " 49  feature_7_A    1844 non-null   float64\n",
            " 50  feature_7_B    1844 non-null   float64\n",
            " 51  feature_7_C    1840 non-null   float64\n",
            " 52  feature_7_D    1840 non-null   float64\n",
            " 53  feature_7_E    1742 non-null   float64\n",
            " 54  feature_7_F    1844 non-null   float64\n",
            " 55  feature_7_G    1844 non-null   float64\n",
            " 56  feature_8_A    1844 non-null   float64\n",
            " 57  feature_8_B    1844 non-null   float64\n",
            " 58  feature_8_C    1840 non-null   float64\n",
            " 59  feature_8_D    1840 non-null   float64\n",
            " 60  feature_8_E    1742 non-null   float64\n",
            " 61  feature_8_F    1844 non-null   float64\n",
            " 62  feature_8_G    1844 non-null   float64\n",
            " 63  feature_9_A    1843 non-null   float64\n",
            " 64  feature_9_B    1843 non-null   float64\n",
            " 65  feature_9_C    1839 non-null   float64\n",
            " 66  feature_9_D    1839 non-null   float64\n",
            " 67  feature_9_E    1741 non-null   float64\n",
            " 68  feature_9_F    1843 non-null   float64\n",
            " 69  feature_9_G    1843 non-null   float64\n",
            " 70  feature_X_A    2660 non-null   int64  \n",
            " 71  feature_X_B    2660 non-null   float64\n",
            " 72  feature_X_C    2660 non-null   float64\n",
            " 73  feature_X_D    2660 non-null   float64\n",
            " 74  index_1        95 non-null     float64\n",
            " 75  index_2        95 non-null     float64\n",
            " 76  index_3        95 non-null     float64\n",
            " 77  Target         2660 non-null   int64  \n",
            "dtypes: float64(75), int64(3)\n",
            "memory usage: 1.6 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Shape\n",
        "df.shape\n",
        "\n",
        "# We have 2660 rows and 78 columns including target variable and ID"
      ],
      "metadata": {
        "id": "mZ6irLAQrIb2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2782273f-feaf-44d6-d375-469ee2cc2de5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2660, 78)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum().sum() # We have a total of 69101 missing values which needs preprocessing"
      ],
      "metadata": {
        "id": "ItQID935s8LA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e52a86f3-2b74-43f5-9d56-9c8a7e0e68de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "69101"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_todrop = []\n",
        "for column in df.columns:\n",
        "    non_null_count = df[column].notnull().sum()  # Count non-null values in the column\n",
        "\n",
        "    # Check if the number of non-null values is less than 200\n",
        "    if non_null_count < 200:\n",
        "        print(\"Column '{}' has less than 200 non-null values.\".format(column))\n",
        "        column_todrop.append(column)\n",
        "print(column_todrop)"
      ],
      "metadata": {
        "id": "gOT5EcZRvy-j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "089b7187-8f24-4aa7-9ae1-c493d0899aa4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column 'TR_1_EventInd' has less than 200 non-null values.\n",
            "Column 'TR_2_EventInd' has less than 200 non-null values.\n",
            "Column 'TR_3_EventInd' has less than 200 non-null values.\n",
            "Column 'index_1' has less than 200 non-null values.\n",
            "Column 'index_2' has less than 200 non-null values.\n",
            "Column 'index_3' has less than 200 non-null values.\n",
            "['TR_1_EventInd', 'TR_2_EventInd', 'TR_3_EventInd', 'index_1', 'index_2', 'index_3']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.drop(columns=column_todrop)\n",
        "df1.info()"
      ],
      "metadata": {
        "id": "o2G7f8joS4qt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b806b683-5581-440b-ca25-56142f681bfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2660 entries, 0 to 2659\n",
            "Data columns (total 72 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   ID            2660 non-null   int64  \n",
            " 1   feature_10_A  2212 non-null   float64\n",
            " 2   feature_10_B  1843 non-null   float64\n",
            " 3   feature_10_F  2212 non-null   float64\n",
            " 4   feature_10_G  2212 non-null   float64\n",
            " 5   feature_1_A   1844 non-null   float64\n",
            " 6   feature_1_B   1844 non-null   float64\n",
            " 7   feature_1_C   1840 non-null   float64\n",
            " 8   feature_1_D   1840 non-null   float64\n",
            " 9   feature_1_E   1742 non-null   float64\n",
            " 10  feature_1_F   1844 non-null   float64\n",
            " 11  feature_1_G   1844 non-null   float64\n",
            " 12  feature_2_A   1844 non-null   float64\n",
            " 13  feature_2_B   1844 non-null   float64\n",
            " 14  feature_2_C   1840 non-null   float64\n",
            " 15  feature_2_D   1840 non-null   float64\n",
            " 16  feature_2_E   1742 non-null   float64\n",
            " 17  feature_2_F   1844 non-null   float64\n",
            " 18  feature_2_G   1844 non-null   float64\n",
            " 19  feature_3_A   1844 non-null   float64\n",
            " 20  feature_3_B   1844 non-null   float64\n",
            " 21  feature_3_C   1840 non-null   float64\n",
            " 22  feature_3_D   1840 non-null   float64\n",
            " 23  feature_3_E   1742 non-null   float64\n",
            " 24  feature_3_F   1844 non-null   float64\n",
            " 25  feature_3_G   1844 non-null   float64\n",
            " 26  feature_4_A   1915 non-null   float64\n",
            " 27  feature_4_B   1912 non-null   float64\n",
            " 28  feature_4_C   1884 non-null   float64\n",
            " 29  feature_4_E   1513 non-null   float64\n",
            " 30  feature_4_F   1915 non-null   float64\n",
            " 31  feature_4_G   1915 non-null   float64\n",
            " 32  feature_5_A   1843 non-null   float64\n",
            " 33  feature_5_B   1843 non-null   float64\n",
            " 34  feature_5_C   1839 non-null   float64\n",
            " 35  feature_5_D   1839 non-null   float64\n",
            " 36  feature_5_E   1741 non-null   float64\n",
            " 37  feature_5_F   1843 non-null   float64\n",
            " 38  feature_5_G   1843 non-null   float64\n",
            " 39  feature_6_A   1844 non-null   float64\n",
            " 40  feature_6_B   1844 non-null   float64\n",
            " 41  feature_6_C   1840 non-null   float64\n",
            " 42  feature_6_D   1840 non-null   float64\n",
            " 43  feature_6_E   1742 non-null   float64\n",
            " 44  feature_6_F   1844 non-null   float64\n",
            " 45  feature_6_G   1844 non-null   float64\n",
            " 46  feature_7_A   1844 non-null   float64\n",
            " 47  feature_7_B   1844 non-null   float64\n",
            " 48  feature_7_C   1840 non-null   float64\n",
            " 49  feature_7_D   1840 non-null   float64\n",
            " 50  feature_7_E   1742 non-null   float64\n",
            " 51  feature_7_F   1844 non-null   float64\n",
            " 52  feature_7_G   1844 non-null   float64\n",
            " 53  feature_8_A   1844 non-null   float64\n",
            " 54  feature_8_B   1844 non-null   float64\n",
            " 55  feature_8_C   1840 non-null   float64\n",
            " 56  feature_8_D   1840 non-null   float64\n",
            " 57  feature_8_E   1742 non-null   float64\n",
            " 58  feature_8_F   1844 non-null   float64\n",
            " 59  feature_8_G   1844 non-null   float64\n",
            " 60  feature_9_A   1843 non-null   float64\n",
            " 61  feature_9_B   1843 non-null   float64\n",
            " 62  feature_9_C   1839 non-null   float64\n",
            " 63  feature_9_D   1839 non-null   float64\n",
            " 64  feature_9_E   1741 non-null   float64\n",
            " 65  feature_9_F   1843 non-null   float64\n",
            " 66  feature_9_G   1843 non-null   float64\n",
            " 67  feature_X_A   2660 non-null   int64  \n",
            " 68  feature_X_B   2660 non-null   float64\n",
            " 69  feature_X_C   2660 non-null   float64\n",
            " 70  feature_X_D   2660 non-null   float64\n",
            " 71  Target        2660 non-null   int64  \n",
            "dtypes: float64(69), int64(3)\n",
            "memory usage: 1.5 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.isna().sum().sum() # Dropping those columns has brought down the null value count to 53657"
      ],
      "metadata": {
        "id": "aE8yF4wUU3kB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c709f97-8760-4558-c2b4-5225b3da3081"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53657"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.describe()"
      ],
      "metadata": {
        "id": "AwkBouVyU1UZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "808b4a0e-4e8c-4290-e8a1-e37b571fab22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                ID  feature_10_A  feature_10_B  feature_10_F  feature_10_G  \\\n",
              "count  2660.000000   2212.000000   1843.000000        2212.0   2212.000000   \n",
              "mean   1330.500000      0.394213      0.027890           0.0      0.457052   \n",
              "std     768.020182      0.488792      0.024328           0.0      0.788577   \n",
              "min       1.000000      0.000000      0.000000           0.0      0.000000   \n",
              "25%     665.750000      0.000000      0.015000           0.0      0.000000   \n",
              "50%    1330.500000      0.000000      0.022000           0.0      0.000000   \n",
              "75%    1995.250000      1.000000      0.033000           0.0      1.000000   \n",
              "max    2660.000000      1.000000      0.419000           0.0      4.000000   \n",
              "\n",
              "       feature_1_A  feature_1_B  feature_1_C  feature_1_D  feature_1_E  ...  \\\n",
              "count  1844.000000  1844.000000  1840.000000  1840.000000  1742.000000  ...   \n",
              "mean      0.480477     0.008254     0.093747    -0.024525   143.184320  ...   \n",
              "std       0.499754     0.004721     1.255044     1.010803    25.966156  ...   \n",
              "min       0.000000     0.002000    -3.217639    -2.707420   106.240714  ...   \n",
              "25%       0.000000     0.005000    -0.945444    -0.759820   119.694893  ...   \n",
              "50%       0.000000     0.007000     0.145855    -0.231405   134.297750  ...   \n",
              "75%       1.000000     0.010000     1.110579     0.568451   169.584250  ...   \n",
              "max       1.000000     0.054000     2.955631     3.283537   182.722000  ...   \n",
              "\n",
              "       feature_9_C  feature_9_D  feature_9_E  feature_9_F  feature_9_G  \\\n",
              "count  1839.000000  1839.000000  1741.000000  1843.000000  1843.000000   \n",
              "mean     -0.032864    -0.004353     3.975986     0.385784     0.880087   \n",
              "std       1.244362     1.021335     1.465224     0.486912     1.320616   \n",
              "min      -2.978773    -2.104279     1.843000     0.000000     0.000000   \n",
              "25%      -1.066589    -0.758685     2.506429     0.000000     0.000000   \n",
              "50%      -0.100283    -0.221294     3.859286     0.000000     0.000000   \n",
              "75%       0.992874     0.534723     5.131786     1.000000     1.000000   \n",
              "max       3.340790     3.404366     7.257000     1.000000    12.000000   \n",
              "\n",
              "       feature_X_A  feature_X_B  feature_X_C  feature_X_D       Target  \n",
              "count  2660.000000  2660.000000  2660.000000  2660.000000  2660.000000  \n",
              "mean      0.525564     0.044619     0.215647     0.040449     0.567669  \n",
              "std       0.499440     0.037747     1.260185     1.054915     0.495493  \n",
              "min       0.000000     0.003000    -3.360669    -2.381170     0.000000  \n",
              "25%       0.000000     0.020000    -0.785635    -0.728389     0.000000  \n",
              "50%       1.000000     0.034000     0.253407    -0.134176     1.000000  \n",
              "75%       1.000000     0.056000     1.253811     0.674400     1.000000  \n",
              "max       1.000000     0.388000     3.038010     3.405693     1.000000  \n",
              "\n",
              "[8 rows x 72 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd8ab34c-df0a-4345-a70d-e8ca25170c81\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>feature_10_A</th>\n",
              "      <th>feature_10_B</th>\n",
              "      <th>feature_10_F</th>\n",
              "      <th>feature_10_G</th>\n",
              "      <th>feature_1_A</th>\n",
              "      <th>feature_1_B</th>\n",
              "      <th>feature_1_C</th>\n",
              "      <th>feature_1_D</th>\n",
              "      <th>feature_1_E</th>\n",
              "      <th>...</th>\n",
              "      <th>feature_9_C</th>\n",
              "      <th>feature_9_D</th>\n",
              "      <th>feature_9_E</th>\n",
              "      <th>feature_9_F</th>\n",
              "      <th>feature_9_G</th>\n",
              "      <th>feature_X_A</th>\n",
              "      <th>feature_X_B</th>\n",
              "      <th>feature_X_C</th>\n",
              "      <th>feature_X_D</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2660.000000</td>\n",
              "      <td>2212.000000</td>\n",
              "      <td>1843.000000</td>\n",
              "      <td>2212.0</td>\n",
              "      <td>2212.000000</td>\n",
              "      <td>1844.000000</td>\n",
              "      <td>1844.000000</td>\n",
              "      <td>1840.000000</td>\n",
              "      <td>1840.000000</td>\n",
              "      <td>1742.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1839.000000</td>\n",
              "      <td>1839.000000</td>\n",
              "      <td>1741.000000</td>\n",
              "      <td>1843.000000</td>\n",
              "      <td>1843.000000</td>\n",
              "      <td>2660.000000</td>\n",
              "      <td>2660.000000</td>\n",
              "      <td>2660.000000</td>\n",
              "      <td>2660.000000</td>\n",
              "      <td>2660.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1330.500000</td>\n",
              "      <td>0.394213</td>\n",
              "      <td>0.027890</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.457052</td>\n",
              "      <td>0.480477</td>\n",
              "      <td>0.008254</td>\n",
              "      <td>0.093747</td>\n",
              "      <td>-0.024525</td>\n",
              "      <td>143.184320</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.032864</td>\n",
              "      <td>-0.004353</td>\n",
              "      <td>3.975986</td>\n",
              "      <td>0.385784</td>\n",
              "      <td>0.880087</td>\n",
              "      <td>0.525564</td>\n",
              "      <td>0.044619</td>\n",
              "      <td>0.215647</td>\n",
              "      <td>0.040449</td>\n",
              "      <td>0.567669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>768.020182</td>\n",
              "      <td>0.488792</td>\n",
              "      <td>0.024328</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.788577</td>\n",
              "      <td>0.499754</td>\n",
              "      <td>0.004721</td>\n",
              "      <td>1.255044</td>\n",
              "      <td>1.010803</td>\n",
              "      <td>25.966156</td>\n",
              "      <td>...</td>\n",
              "      <td>1.244362</td>\n",
              "      <td>1.021335</td>\n",
              "      <td>1.465224</td>\n",
              "      <td>0.486912</td>\n",
              "      <td>1.320616</td>\n",
              "      <td>0.499440</td>\n",
              "      <td>0.037747</td>\n",
              "      <td>1.260185</td>\n",
              "      <td>1.054915</td>\n",
              "      <td>0.495493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.002000</td>\n",
              "      <td>-3.217639</td>\n",
              "      <td>-2.707420</td>\n",
              "      <td>106.240714</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.978773</td>\n",
              "      <td>-2.104279</td>\n",
              "      <td>1.843000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003000</td>\n",
              "      <td>-3.360669</td>\n",
              "      <td>-2.381170</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>665.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.005000</td>\n",
              "      <td>-0.945444</td>\n",
              "      <td>-0.759820</td>\n",
              "      <td>119.694893</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.066589</td>\n",
              "      <td>-0.758685</td>\n",
              "      <td>2.506429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>-0.785635</td>\n",
              "      <td>-0.728389</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1330.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.022000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007000</td>\n",
              "      <td>0.145855</td>\n",
              "      <td>-0.231405</td>\n",
              "      <td>134.297750</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.100283</td>\n",
              "      <td>-0.221294</td>\n",
              "      <td>3.859286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.034000</td>\n",
              "      <td>0.253407</td>\n",
              "      <td>-0.134176</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1995.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.033000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>1.110579</td>\n",
              "      <td>0.568451</td>\n",
              "      <td>169.584250</td>\n",
              "      <td>...</td>\n",
              "      <td>0.992874</td>\n",
              "      <td>0.534723</td>\n",
              "      <td>5.131786</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.056000</td>\n",
              "      <td>1.253811</td>\n",
              "      <td>0.674400</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2660.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.419000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.054000</td>\n",
              "      <td>2.955631</td>\n",
              "      <td>3.283537</td>\n",
              "      <td>182.722000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.340790</td>\n",
              "      <td>3.404366</td>\n",
              "      <td>7.257000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.388000</td>\n",
              "      <td>3.038010</td>\n",
              "      <td>3.405693</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 72 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd8ab34c-df0a-4345-a70d-e8ca25170c81')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bd8ab34c-df0a-4345-a70d-e8ca25170c81 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bd8ab34c-df0a-4345-a70d-e8ca25170c81');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1cb79f88-45c1-42c6-ab82-2d34abe597a4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1cb79f88-45c1-42c6-ab82-2d34abe597a4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1cb79f88-45c1-42c6-ab82-2d34abe597a4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming df is your DataFrame containing the dataset\n",
        "\n",
        "# Calculate median for all features\n",
        "median_values = df1.median()\n",
        "\n",
        "# Display median values\n",
        "print(\"Median values for each feature:\")\n",
        "print(median_values)\n"
      ],
      "metadata": {
        "id": "vKSET8JXuL-m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fc83378-36a5-4b47-c23f-149c074b3b85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Median values for each feature:\n",
            "ID              1330.500000\n",
            "feature_10_A       0.000000\n",
            "feature_10_B       0.022000\n",
            "feature_10_F       0.000000\n",
            "feature_10_G       0.000000\n",
            "                   ...     \n",
            "feature_X_A        1.000000\n",
            "feature_X_B        0.034000\n",
            "feature_X_C        0.253407\n",
            "feature_X_D       -0.134176\n",
            "Target             1.000000\n",
            "Length: 72, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert median_values Series to DataFrame for better display\n",
        "median_df = median_values.to_frame(name='Median')\n",
        "\n",
        "# Display median values DataFrame\n",
        "print(\"Median values for each feature:\")\n",
        "print(median_df)\n"
      ],
      "metadata": {
        "id": "zb75eLyruHBt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "378c08d5-32e0-4583-f5aa-8df7c33165ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Median values for each feature:\n",
            "                   Median\n",
            "ID            1330.500000\n",
            "feature_10_A     0.000000\n",
            "feature_10_B     0.022000\n",
            "feature_10_F     0.000000\n",
            "feature_10_G     0.000000\n",
            "...                   ...\n",
            "feature_X_A      1.000000\n",
            "feature_X_B      0.034000\n",
            "feature_X_C      0.253407\n",
            "feature_X_D     -0.134176\n",
            "Target           1.000000\n",
            "\n",
            "[72 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#histogram\n",
        "df.hist(figsize=(20,10)) # you can add figsize=(10,10) to resize the figures\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "2J3lJ8jHuixp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "35aa1943-4d24-4074-bac5-4498687f5567"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 81 Axes>"
            ],
            "image/png": 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    },
    {
      "cell_type": "markdown",
      "source": [
        "- Features TR_1_EventInd, TR_2_EventInd, TR_3_EventInd, Feature_10_F do not have any variation. They either have 0s or 1s throughout and do not xontribute to the prediction of the target varibable.\n",
        "- Features index_1, index_2, index_3 maximum of 97 records, contributing to only 3% of the total rows, and the rest of the entries are null. Hence, these features have negligible contribution towards the prediction of our target variable."
      ],
      "metadata": {
        "id": "HtJhPtFAabGR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Creating hold out data set\n",
        "\n",
        "df_train = df.iloc[:-400]\n",
        "df_test = df.iloc[-400:]\n",
        "\n",
        "print(\"Training data set shape:\", df_train.shape)\n",
        "print(\"Testing data set shape:\",df_test.shape)"
      ],
      "metadata": {
        "id": "dV3C-3xEAPIT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bde34dfa-3b0f-41e5-ffd9-ea16353ac9de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data set shape: (2260, 78)\n",
            "Testing data set shape: (400, 78)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking if df_train's last row and df_test's first row are different\n",
        "\n",
        "df_train.tail(n=1)\n"
      ],
      "metadata": {
        "id": "L1Xee2QCA6xX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "outputId": "f2797360-8eda-4e18-eb01-7a2dde14f2a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID  TR_1_EventInd  TR_2_EventInd  TR_3_EventInd  feature_10_A  \\\n",
              "2259  2260            NaN            NaN            NaN           0.0   \n",
              "\n",
              "      feature_10_B  feature_10_F  feature_10_G  feature_1_A  feature_1_B  ...  \\\n",
              "2259         0.032           0.0           0.0          1.0        0.005  ...   \n",
              "\n",
              "      feature_9_F  feature_9_G  feature_X_A  feature_X_B  feature_X_C  \\\n",
              "2259          0.0          1.0            1         0.01     -0.64314   \n",
              "\n",
              "      feature_X_D  index_1  index_2  index_3  Target  \n",
              "2259    -0.575943      NaN      NaN      NaN       0  \n",
              "\n",
              "[1 rows x 78 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3584982a-61bf-4acd-aedc-028273acb1e9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>TR_1_EventInd</th>\n",
              "      <th>TR_2_EventInd</th>\n",
              "      <th>TR_3_EventInd</th>\n",
              "      <th>feature_10_A</th>\n",
              "      <th>feature_10_B</th>\n",
              "      <th>feature_10_F</th>\n",
              "      <th>feature_10_G</th>\n",
              "      <th>feature_1_A</th>\n",
              "      <th>feature_1_B</th>\n",
              "      <th>...</th>\n",
              "      <th>feature_9_F</th>\n",
              "      <th>feature_9_G</th>\n",
              "      <th>feature_X_A</th>\n",
              "      <th>feature_X_B</th>\n",
              "      <th>feature_X_C</th>\n",
              "      <th>feature_X_D</th>\n",
              "      <th>index_1</th>\n",
              "      <th>index_2</th>\n",
              "      <th>index_3</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2259</th>\n",
              "      <td>2260</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.032</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.005</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.64314</td>\n",
              "      <td>-0.575943</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 78 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3584982a-61bf-4acd-aedc-028273acb1e9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3584982a-61bf-4acd-aedc-028273acb1e9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3584982a-61bf-4acd-aedc-028273acb1e9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.head(n=1)"
      ],
      "metadata": {
        "id": "pKQGFUk3B3j5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "outputId": "4dc01132-cdac-47bf-baf5-6b96bf2d63dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID  TR_1_EventInd  TR_2_EventInd  TR_3_EventInd  feature_10_A  \\\n",
              "2260  2261            NaN            NaN            NaN           0.0   \n",
              "\n",
              "      feature_10_B  feature_10_F  feature_10_G  feature_1_A  feature_1_B  ...  \\\n",
              "2260         0.044           0.0           0.0          1.0        0.006  ...   \n",
              "\n",
              "      feature_9_F  feature_9_G  feature_X_A  feature_X_B  feature_X_C  \\\n",
              "2260          1.0          1.0            1        0.042      1.03621   \n",
              "\n",
              "      feature_X_D  index_1  index_2  index_3  Target  \n",
              "2260    -0.227726      NaN      NaN      NaN       0  \n",
              "\n",
              "[1 rows x 78 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f1534939-d5f6-4afc-8e60-e4f63381b730\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>TR_1_EventInd</th>\n",
              "      <th>TR_2_EventInd</th>\n",
              "      <th>TR_3_EventInd</th>\n",
              "      <th>feature_10_A</th>\n",
              "      <th>feature_10_B</th>\n",
              "      <th>feature_10_F</th>\n",
              "      <th>feature_10_G</th>\n",
              "      <th>feature_1_A</th>\n",
              "      <th>feature_1_B</th>\n",
              "      <th>...</th>\n",
              "      <th>feature_9_F</th>\n",
              "      <th>feature_9_G</th>\n",
              "      <th>feature_X_A</th>\n",
              "      <th>feature_X_B</th>\n",
              "      <th>feature_X_C</th>\n",
              "      <th>feature_X_D</th>\n",
              "      <th>index_1</th>\n",
              "      <th>index_2</th>\n",
              "      <th>index_3</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2260</th>\n",
              "      <td>2261</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.006</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.042</td>\n",
              "      <td>1.03621</td>\n",
              "      <td>-0.227726</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 78 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1534939-d5f6-4afc-8e60-e4f63381b730')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f1534939-d5f6-4afc-8e60-e4f63381b730 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f1534939-d5f6-4afc-8e60-e4f63381b730');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['Target'].value_counts()\n"
      ],
      "metadata": {
        "id": "pS6lpD4gvv2g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85724200-d3ce-4aaf-a634-19624e8cf1f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Target\n",
              "1    1285\n",
              "0     975\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the count of each class in the target variable\n",
        "class_counts = df_train['Target'].value_counts()\n",
        "\n",
        "# Create a figure with two subplots\n",
        "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
        "\n",
        "# Count graph\n",
        "class_counts.plot(kind='bar', ax=axs[0], color='skyblue')\n",
        "axs[0].set_title('Count of Each Class')\n",
        "axs[0].set_ylabel('Count')\n",
        "axs[0].set_xlabel('Target')\n",
        "axs[0].set_xticklabels(class_counts.index, rotation=0)\n",
        "\n",
        "# Pie chart\n",
        "axs[1].pie(class_counts, labels=class_counts.index, autopct='%1.1f%%', startangle=140)\n",
        "axs[1].set_title('Distribution of Target Variable')\n",
        "axs[1].axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "\n",
        "# Add count values on top of each bar\n",
        "for i, count in enumerate(class_counts):\n",
        "    axs[0].text(i, count + 5, str(count), ha='center', va='bottom')\n",
        "\n",
        "# Add legends\n",
        "axs[1].legend(title='Classes', loc='upper right')\n",
        "axs[0].legend(['Count'], loc='upper right')\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "R7TJ5xzOLaul",
        "outputId": "8735eed8-9597-4e97-9d77-4ed08cb16175"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.isna().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ai3_VWvp_EVi",
        "outputId": "9dccfff8-ad91-4040-9d6c-67cbecbb2a93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59022"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Imputing the Nans with 0s\n",
        "tmp = df_train.copy()\n",
        "tmp.fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "xhjxV4SaLpYY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Imputing the Nans with 0s\n",
        "tmp1 = df_test.copy()\n",
        "tmp1.fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "E66RCMxejyCC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Imputing the Nans with median value\n",
        "\n",
        "tmp = df_train.copy()\n",
        "# Fill NaN values with the median of each column\n",
        "tmp.fillna(tmp.median(), inplace=True)\n",
        "\n",
        "tmp1 = df_test.copy()\n",
        "# Fill NaN values with the median of each column\n",
        "tmp1.fillna(tmp.median(), inplace=True)"
      ],
      "metadata": {
        "id": "qOtkdCmqYljL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tmp.isna().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nMEVoXYz9kNm",
        "outputId": "790830ea-da85-400f-9a74-9838d793f6d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tmp2 = tmp.copy()\n",
        "print(tmp2.shape)\n",
        "tmp2 = tmp2.drop(columns=column_todrop)\n",
        "print(tmp2.shape)\n",
        "tmp3 = tmp1.copy()\n",
        "print(tmp3.shape)\n",
        "tmp3 = tmp3.drop(columns=column_todrop)\n",
        "print(tmp3.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVJX9FX6-Y8M",
        "outputId": "1e4409d3-a492-4a78-efab-bd73434e3922"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2260, 78)\n",
            "(2260, 72)\n",
            "(400, 78)\n",
            "(400, 72)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tmp2.columns)\n",
        "print(tmp3.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23BNny9uXB8z",
        "outputId": "3474aba5-436b-49e6-a4a3-b1a5e509d2a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['ID', 'feature_10_A', 'feature_10_B', 'feature_10_F', 'feature_10_G',\n",
            "       'feature_1_A', 'feature_1_B', 'feature_1_C', 'feature_1_D',\n",
            "       'feature_1_E', 'feature_1_F', 'feature_1_G', 'feature_2_A',\n",
            "       'feature_2_B', 'feature_2_C', 'feature_2_D', 'feature_2_E',\n",
            "       'feature_2_F', 'feature_2_G', 'feature_3_A', 'feature_3_B',\n",
            "       'feature_3_C', 'feature_3_D', 'feature_3_E', 'feature_3_F',\n",
            "       'feature_3_G', 'feature_4_A', 'feature_4_B', 'feature_4_C',\n",
            "       'feature_4_E', 'feature_4_F', 'feature_4_G', 'feature_5_A',\n",
            "       'feature_5_B', 'feature_5_C', 'feature_5_D', 'feature_5_E',\n",
            "       'feature_5_F', 'feature_5_G', 'feature_6_A', 'feature_6_B',\n",
            "       'feature_6_C', 'feature_6_D', 'feature_6_E', 'feature_6_F',\n",
            "       'feature_6_G', 'feature_7_A', 'feature_7_B', 'feature_7_C',\n",
            "       'feature_7_D', 'feature_7_E', 'feature_7_F', 'feature_7_G',\n",
            "       'feature_8_A', 'feature_8_B', 'feature_8_C', 'feature_8_D',\n",
            "       'feature_8_E', 'feature_8_F', 'feature_8_G', 'feature_9_A',\n",
            "       'feature_9_B', 'feature_9_C', 'feature_9_D', 'feature_9_E',\n",
            "       'feature_9_F', 'feature_9_G', 'feature_X_A', 'feature_X_B',\n",
            "       'feature_X_C', 'feature_X_D', 'Target'],\n",
            "      dtype='object')\n",
            "Index(['ID', 'feature_10_A', 'feature_10_B', 'feature_10_F', 'feature_10_G',\n",
            "       'feature_1_A', 'feature_1_B', 'feature_1_C', 'feature_1_D',\n",
            "       'feature_1_E', 'feature_1_F', 'feature_1_G', 'feature_2_A',\n",
            "       'feature_2_B', 'feature_2_C', 'feature_2_D', 'feature_2_E',\n",
            "       'feature_2_F', 'feature_2_G', 'feature_3_A', 'feature_3_B',\n",
            "       'feature_3_C', 'feature_3_D', 'feature_3_E', 'feature_3_F',\n",
            "       'feature_3_G', 'feature_4_A', 'feature_4_B', 'feature_4_C',\n",
            "       'feature_4_E', 'feature_4_F', 'feature_4_G', 'feature_5_A',\n",
            "       'feature_5_B', 'feature_5_C', 'feature_5_D', 'feature_5_E',\n",
            "       'feature_5_F', 'feature_5_G', 'feature_6_A', 'feature_6_B',\n",
            "       'feature_6_C', 'feature_6_D', 'feature_6_E', 'feature_6_F',\n",
            "       'feature_6_G', 'feature_7_A', 'feature_7_B', 'feature_7_C',\n",
            "       'feature_7_D', 'feature_7_E', 'feature_7_F', 'feature_7_G',\n",
            "       'feature_8_A', 'feature_8_B', 'feature_8_C', 'feature_8_D',\n",
            "       'feature_8_E', 'feature_8_F', 'feature_8_G', 'feature_9_A',\n",
            "       'feature_9_B', 'feature_9_C', 'feature_9_D', 'feature_9_E',\n",
            "       'feature_9_F', 'feature_9_G', 'feature_X_A', 'feature_X_B',\n",
            "       'feature_X_C', 'feature_X_D', 'Target'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pQngtWhdjr7f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest"
      ],
      "metadata": {
        "id": "1Wr5jHjHoT12"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp1.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp1['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the Random Forest classifier\n",
        "RF = RandomForestClassifier(n_estimators=100, random_state=999)\n",
        "\n",
        "# Train the Random Forest classifier\n",
        "RF.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = RF.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GtAQ4NAE8Jjo",
        "outputId": "02fb1700-871c-4b40-cf7f-f3062b65cc07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[111  64]\n",
            " [ 44 181]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.63      0.67       175\n",
            "           1       0.74      0.80      0.77       225\n",
            "\n",
            "    accuracy                           0.73       400\n",
            "   macro avg       0.73      0.72      0.72       400\n",
            "weighted avg       0.73      0.73      0.73       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the Random Forest classifier\n",
        "RF = RandomForestClassifier(n_estimators=100, random_state=999)\n",
        "\n",
        "# Train the Random Forest classifier\n",
        "RF.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = RF.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gHouChfm8oFB",
        "outputId": "fc1d5ca1-a158-46d2-c7b2-927203b257b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[118  57]\n",
            " [ 44 181]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.67      0.70       175\n",
            "           1       0.76      0.80      0.78       225\n",
            "\n",
            "    accuracy                           0.75       400\n",
            "   macro avg       0.74      0.74      0.74       400\n",
            "weighted avg       0.75      0.75      0.75       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accuracy 75%"
      ],
      "metadata": {
        "id": "g5Vcv2kgfqyB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_columns = tmp1.drop(['ID','Target'], axis=1).columns"
      ],
      "metadata": {
        "id": "OiRs1bl2DGAo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6Csucb0DVtx",
        "outputId": "b6218003-442d-487e-a656-d4924d387021"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['TR_1_EventInd', 'TR_2_EventInd', 'TR_3_EventInd', 'feature_10_A',\n",
              "       'feature_10_B', 'feature_10_F', 'feature_10_G', 'feature_1_A',\n",
              "       'feature_1_B', 'feature_1_C', 'feature_1_D', 'feature_1_E',\n",
              "       'feature_1_F', 'feature_1_G', 'feature_2_A', 'feature_2_B',\n",
              "       'feature_2_C', 'feature_2_D', 'feature_2_E', 'feature_2_F',\n",
              "       'feature_2_G', 'feature_3_A', 'feature_3_B', 'feature_3_C',\n",
              "       'feature_3_D', 'feature_3_E', 'feature_3_F', 'feature_3_G',\n",
              "       'feature_4_A', 'feature_4_B', 'feature_4_C', 'feature_4_E',\n",
              "       'feature_4_F', 'feature_4_G', 'feature_5_A', 'feature_5_B',\n",
              "       'feature_5_C', 'feature_5_D', 'feature_5_E', 'feature_5_F',\n",
              "       'feature_5_G', 'feature_6_A', 'feature_6_B', 'feature_6_C',\n",
              "       'feature_6_D', 'feature_6_E', 'feature_6_F', 'feature_6_G',\n",
              "       'feature_7_A', 'feature_7_B', 'feature_7_C', 'feature_7_D',\n",
              "       'feature_7_E', 'feature_7_F', 'feature_7_G', 'feature_8_A',\n",
              "       'feature_8_B', 'feature_8_C', 'feature_8_D', 'feature_8_E',\n",
              "       'feature_8_F', 'feature_8_G', 'feature_9_A', 'feature_9_B',\n",
              "       'feature_9_C', 'feature_9_D', 'feature_9_E', 'feature_9_F',\n",
              "       'feature_9_G', 'feature_X_A', 'feature_X_B', 'feature_X_C',\n",
              "       'feature_X_D', 'index_1', 'index_2', 'index_3'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = RF\n",
        "\n",
        "result = permutation_importance(clf, X_test, y_test, n_repeats=15,\n",
        "                                random_state=999)\n",
        "perm_sorted_idx = result.importances_mean.argsort()\n",
        "\n",
        "tree_importance_sorted_idx = np.argsort(clf.feature_importances_)\n",
        "tree_indices = np.arange(0, len(clf.feature_importances_)) + 0.5\n",
        "\n",
        "fig, ax1 = plt.subplots(1, 1, figsize=(30,30))\n",
        "ax1.boxplot(result.importances[perm_sorted_idx].T, vert=False,\n",
        "            labels=X_columns[perm_sorted_idx])\n",
        "fig.suptitle('RFC Feature Importance', y=1.05)\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SZaR6YI1BA_k",
        "outputId": "3e351220-924a-4670-cff7-b861492701c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x3000 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decision Tree"
      ],
      "metadata": {
        "id": "h0Uo3k1soXVS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the Decision Tree classifier\n",
        "DT = DecisionTreeClassifier(random_state=999)\n",
        "\n",
        "# Train the Decision Tree classifier\n",
        "DT.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = DT.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iuTRb71hoYlm",
        "outputId": "18810ee4-8920-4208-a79d-8c0330429c8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[116  59]\n",
            " [ 71 154]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.62      0.66      0.64       175\n",
            "           1       0.72      0.68      0.70       225\n",
            "\n",
            "    accuracy                           0.68       400\n",
            "   macro avg       0.67      0.67      0.67       400\n",
            "weighted avg       0.68      0.68      0.68       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accuracy 68%"
      ],
      "metadata": {
        "id": "mOu5pSxSf8wl"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UhBs1UnhonMa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Lostic Regression"
      ],
      "metadata": {
        "id": "eNG1MujhorR8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the Logistic Regression classifier\n",
        "LR = LogisticRegression(random_state=999)\n",
        "\n",
        "# Train the Logistic Regression classifier\n",
        "LR.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = LR.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JxVRIlX6pCOA",
        "outputId": "83ed914e-f751-4371-a17b-864baf322171"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[ 49 126]\n",
            " [ 37 188]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      0.28      0.38       175\n",
            "           1       0.60      0.84      0.70       225\n",
            "\n",
            "    accuracy                           0.59       400\n",
            "   macro avg       0.58      0.56      0.54       400\n",
            "weighted avg       0.59      0.59      0.56       400\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accuracy 59%"
      ],
      "metadata": {
        "id": "9nCufrp-gBVX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Gradient Boosting Classifier"
      ],
      "metadata": {
        "id": "KjP6dtzppNv8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the Gradient Boosting classifier\n",
        "GBM = GradientBoostingClassifier(n_estimators=100, random_state=999)\n",
        "\n",
        "# Train the Gradient Boosting classifier\n",
        "GBM.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = GBM.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OnIjUSMspJeQ",
        "outputId": "e156807d-af3f-486f-99fa-e55d26d0821c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[ 93  82]\n",
            " [ 29 196]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.53      0.63       175\n",
            "           1       0.71      0.87      0.78       225\n",
            "\n",
            "    accuracy                           0.72       400\n",
            "   macro avg       0.73      0.70      0.70       400\n",
            "weighted avg       0.73      0.72      0.71       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accuracy 72%"
      ],
      "metadata": {
        "id": "UpIZAlQJgHzi"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w3g1mxzQpqLu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Support Vector Machine"
      ],
      "metadata": {
        "id": "jexb4msLpus0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the Support Vector Machine classifier\n",
        "svm_classifier = SVC(kernel='rbf', random_state=999)\n",
        "\n",
        "# Train the Support Vector Machine classifier\n",
        "svm_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = svm_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L9-mJbg6p6aa",
        "outputId": "5369f402-686d-4987-9ba8-05a4d4b42ec7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[ 45 130]\n",
            " [ 25 200]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      0.26      0.37       175\n",
            "           1       0.61      0.89      0.72       225\n",
            "\n",
            "    accuracy                           0.61       400\n",
            "   macro avg       0.62      0.57      0.54       400\n",
            "weighted avg       0.62      0.61      0.57       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accuracy 61%"
      ],
      "metadata": {
        "id": "rbAJ5ezDgn-M"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CbpoGPSsp_rR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# K-Nearest Neighbors"
      ],
      "metadata": {
        "id": "5HjESAhuqc89"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the K-Nearest Neighbors classifier\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=5)\n",
        "\n",
        "# Train the K-Nearest Neighbors classifier\n",
        "knn_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = knn_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gaGoRKZiqf5J",
        "outputId": "196fb837-cb46-4785-e559-e22057ebec06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[ 83  92]\n",
            " [ 68 157]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.55      0.47      0.51       175\n",
            "           1       0.63      0.70      0.66       225\n",
            "\n",
            "    accuracy                           0.60       400\n",
            "   macro avg       0.59      0.59      0.59       400\n",
            "weighted avg       0.60      0.60      0.60       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accuracy 60%"
      ],
      "metadata": {
        "id": "gDseOn0_gtqx"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nm5Lj-Gwqkv-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Naive Bayes"
      ],
      "metadata": {
        "id": "LawfL-3PqmQM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the Naive Bayes classifier (Gaussian Naive Bayes)\n",
        "nb_classifier = GaussianNB()\n",
        "\n",
        "# Train the Naive Bayes classifier\n",
        "nb_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = nb_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnJgYDh0qwdD",
        "outputId": "c2a7d58c-17f5-4dfe-9a81-3a652eff147e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[120  55]\n",
            " [133  92]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.47      0.69      0.56       175\n",
            "           1       0.63      0.41      0.49       225\n",
            "\n",
            "    accuracy                           0.53       400\n",
            "   macro avg       0.55      0.55      0.53       400\n",
            "weighted avg       0.56      0.53      0.52       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accuracy 53%"
      ],
      "metadata": {
        "id": "xV2omZNDgyku"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O9kKohjUqyD4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# XG Boost"
      ],
      "metadata": {
        "id": "18ILLNuAq5P9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install xgboost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkOeeP6yrGEU",
        "outputId": "89cf9a49-18e0-42a7-9f1f-3beb23def037"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (2.0.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.11.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the XGBoost classifier\n",
        "xgb_classifier = XGBClassifier(random_state=999)\n",
        "\n",
        "# Train the XGBoost classifier\n",
        "xgb_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = xgb_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TetDd1KxrG2U",
        "outputId": "c52c916a-ca05-43f3-c669-4185dd9e96a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[119  56]\n",
            " [ 42 183]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.74      0.68      0.71       175\n",
            "           1       0.77      0.81      0.79       225\n",
            "\n",
            "    accuracy                           0.76       400\n",
            "   macro avg       0.75      0.75      0.75       400\n",
            "weighted avg       0.75      0.76      0.75       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accuracy 76%"
      ],
      "metadata": {
        "id": "NcXr43uMg6YR"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SIhAqQPdrLia"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Light GBM"
      ],
      "metadata": {
        "id": "re6gti9Qrdpu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install lightgbm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oNv-oiolrgWY",
        "outputId": "5d6a3b21-4655-483b-ada2-34ecd6e4a0d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.10/dist-packages (4.1.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from lightgbm) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from lightgbm) (1.11.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from lightgbm import LGBMClassifier\n",
        "\n",
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the LightGBM classifier\n",
        "lgbm_classifier = LGBMClassifier(random_state=999)\n",
        "\n",
        "# Train the LightGBM classifier\n",
        "lgbm_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = lgbm_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vrOKiN2IrhJU",
        "outputId": "00010748-5d40-47ed-ea8a-06b0e9b1e260"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Number of positive: 1285, number of negative: 975\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001231 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 8025\n",
            "[LightGBM] [Info] Number of data points in the train set: 2260, number of used features: 69\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.568584 -> initscore=0.276077\n",
            "[LightGBM] [Info] Start training from score 0.276077\n",
            "Confusion Matrix:\n",
            "[[114  61]\n",
            " [ 53 172]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.65      0.67       175\n",
            "           1       0.74      0.76      0.75       225\n",
            "\n",
            "    accuracy                           0.71       400\n",
            "   macro avg       0.71      0.71      0.71       400\n",
            "weighted avg       0.71      0.71      0.71       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accuracy 71%"
      ],
      "metadata": {
        "id": "U6Egf3yRhAbC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Initialize SMOTE\n",
        "smote = SMOTE(random_state=999)\n",
        "\n",
        "# Perform SMOTE on the training data only\n",
        "X_train, y_train = smote.fit_resample(X_train, y_train)\n",
        "\n",
        "\n",
        "\n",
        "y_train.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNFEdxXurkzC",
        "outputId": "1d86832a-3239-4f6b-b0db-0cac6c0bd92a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Target\n",
              "0    1285\n",
              "1    1285\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install imbalanced-learn\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RNpaGV1t83R",
        "outputId": "66c18706-aa3e-4b02-a42e-8a6f9e7b3edc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.11.4)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.4.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (3.4.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the LightGBM classifier\n",
        "lgbm_classifier = LGBMClassifier(random_state=999)\n",
        "\n",
        "# Train the LightGBM classifier\n",
        "lgbm_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = lgbm_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "akoMGV5ns4qk",
        "outputId": "f567ebe7-0279-4766-dedc-e1e3900ece2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Number of positive: 1285, number of negative: 1285\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004181 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 9888\n",
            "[LightGBM] [Info] Number of data points in the train set: 2570, number of used features: 69\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n",
            "Confusion Matrix:\n",
            "[[124  51]\n",
            " [ 58 167]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.71      0.69       175\n",
            "           1       0.77      0.74      0.75       225\n",
            "\n",
            "    accuracy                           0.73       400\n",
            "   macro avg       0.72      0.73      0.72       400\n",
            "weighted avg       0.73      0.73      0.73       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "y_train.value_counts()\n",
        "# Initialize RandomOverSampler\n",
        "oversampler = RandomOverSampler(random_state=999)\n",
        "\n",
        "# Perform random oversampling on the training data only\n",
        "X_train_balanced, y_train_balanced = oversampler.fit_resample(X_train, y_train)\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the LightGBM classifier\n",
        "lgbm_classifier = LGBMClassifier(random_state=999)\n",
        "\n",
        "# Train the LightGBM classifier\n",
        "lgbm_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = lgbm_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wu12j9UWtPid",
        "outputId": "f54ec5f2-9ef2-40e5-a172-c33bfd19a2a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Number of positive: 1285, number of negative: 975\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001658 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 8025\n",
            "[LightGBM] [Info] Number of data points in the train set: 2260, number of used features: 69\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.568584 -> initscore=0.276077\n",
            "[LightGBM] [Info] Start training from score 0.276077\n",
            "Confusion Matrix:\n",
            "[[114  61]\n",
            " [ 53 172]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.65      0.67       175\n",
            "           1       0.74      0.76      0.75       225\n",
            "\n",
            "    accuracy                           0.71       400\n",
            "   macro avg       0.71      0.71      0.71       400\n",
            "weighted avg       0.71      0.71      0.71       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Smoting produces worse results and over sampling produce similar results. Our data is fairly equally split already, so we are not going to perform over sampling or smoting"
      ],
      "metadata": {
        "id": "CMWvkPR0tkTT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cat Boosting"
      ],
      "metadata": {
        "id": "hR-f_APx35-v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install catboost\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8IT4TcyD4IzT",
        "outputId": "0fceb312-a185-44e2-e92c-8c4616f39395"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting catboost\n",
            "  Downloading catboost-1.2.3-cp310-cp310-manylinux2014_x86_64.whl (98.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.5/98.5 MB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.25.2)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (2.0.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.11.4)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2024.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.51.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (24.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.2)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.2.3)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-1.2.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from catboost import CatBoostClassifier\n",
        "\n",
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the CatBoost classifier\n",
        "catboost_classifier = CatBoostClassifier(random_state=999)\n",
        "\n",
        "# Train the CatBoost classifier\n",
        "catboost_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = catboost_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H-0WKmuPux51",
        "outputId": "06434f10-0e0b-4589-9329-2ab288d1a355"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate set to 0.014593\n",
            "0:\tlearn: 0.6918281\ttotal: 79.7ms\tremaining: 1m 19s\n",
            "1:\tlearn: 0.6893847\ttotal: 102ms\tremaining: 50.9s\n",
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            "983:\tlearn: 0.3549416\ttotal: 11.6s\tremaining: 189ms\n",
            "984:\tlearn: 0.3548179\ttotal: 11.6s\tremaining: 177ms\n",
            "985:\tlearn: 0.3545842\ttotal: 11.6s\tremaining: 165ms\n",
            "986:\tlearn: 0.3543621\ttotal: 11.6s\tremaining: 153ms\n",
            "987:\tlearn: 0.3541921\ttotal: 11.6s\tremaining: 141ms\n",
            "988:\tlearn: 0.3539635\ttotal: 11.6s\tremaining: 130ms\n",
            "989:\tlearn: 0.3538170\ttotal: 11.7s\tremaining: 118ms\n",
            "990:\tlearn: 0.3536368\ttotal: 11.7s\tremaining: 106ms\n",
            "991:\tlearn: 0.3533680\ttotal: 11.7s\tremaining: 94.2ms\n",
            "992:\tlearn: 0.3531954\ttotal: 11.7s\tremaining: 82.5ms\n",
            "993:\tlearn: 0.3530410\ttotal: 11.7s\tremaining: 70.7ms\n",
            "994:\tlearn: 0.3528478\ttotal: 11.7s\tremaining: 58.9ms\n",
            "995:\tlearn: 0.3525517\ttotal: 11.7s\tremaining: 47.1ms\n",
            "996:\tlearn: 0.3523724\ttotal: 11.7s\tremaining: 35.3ms\n",
            "997:\tlearn: 0.3521632\ttotal: 11.8s\tremaining: 23.6ms\n",
            "998:\tlearn: 0.3519417\ttotal: 11.8s\tremaining: 11.8ms\n",
            "999:\tlearn: 0.3517771\ttotal: 11.8s\tremaining: 0us\n",
            "Confusion Matrix:\n",
            "[[ 90  85]\n",
            " [ 29 196]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.51      0.61       175\n",
            "           1       0.70      0.87      0.77       225\n",
            "\n",
            "    accuracy                           0.71       400\n",
            "   macro avg       0.73      0.69      0.69       400\n",
            "weighted avg       0.72      0.71      0.70       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accuracy 71%"
      ],
      "metadata": {
        "id": "DnDNiwBStHub"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rQMz69K238N8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Rough"
      ],
      "metadata": {
        "id": "sNWINajqvqDy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# List of words\n",
        "words = [\n",
        "    \"feature_8_G\", \"feature_3_D\", \"feature_7_E\", \"feature_5_D\", \"feature_8_E\",\n",
        "    \"feature_9_D\", \"feature_5_F\", \"feature_6_D\", \"feature_7_G\",\n",
        "    \"feature_9_C\", \"feature_2_E\", \"feature_2_D\", \"feature_4_B\", \"feature_8_D\",\n",
        "    \"feature_10_A\", \"feature_1_E\", \"feature_2_A\", \"feature_6_C\", \"feature_3_E\",\n",
        "    \"feature_6_E\", \"feature_7_F\", \"feature_1_F\", \"feature_4_E\",\n",
        "    \"feature_3_C\", \"feature_1_D\", \"feature_7_C\", \"feature_1_C\", \"feature_8_C\",\n",
        "    \"feature_7_D\", \"feature_5_E\", \"feature_4_C\", \"feature_10_F\", \"feature_10_B\",\n",
        "    \"feature_9_E\"\n",
        "]\n",
        "\n",
        "# Convert the list of words to a Python list\n",
        "featurestodrop = list(words)"
      ],
      "metadata": {
        "id": "57KovxGBvq8J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "X = X.drop(featurestodrop, axis=1)\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "X_test = X_test.drop(featurestodrop, axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the XGBoost classifier\n",
        "xgb_classifier = XGBClassifier(random_state=999)\n",
        "\n",
        "# Train the XGBoost classifier\n",
        "xgb_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = xgb_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdJHRzyavtVr",
        "outputId": "830cf7fe-62f7-4360-8e32-b05ce8fe6c9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[ 85  90]\n",
            " [ 62 163]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.58      0.49      0.53       175\n",
            "           1       0.64      0.72      0.68       225\n",
            "\n",
            "    accuracy                           0.62       400\n",
            "   macro avg       0.61      0.61      0.60       400\n",
            "weighted avg       0.62      0.62      0.61       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target variable (y)\n",
        "X = tmp2.drop(['ID', 'Target'], axis=1)\n",
        "X = X.drop(featurestodrop, axis=1)\n",
        "\n",
        "y = tmp2['Target']\n",
        "\n",
        "X_train = X\n",
        "y_train = y\n",
        "X_test = tmp3.drop(['ID', 'Target'], axis=1)\n",
        "X_test = X_test.drop(featurestodrop, axis=1)\n",
        "y_test = tmp3['Target']\n",
        "\n",
        "# Setting random seed\n",
        "rng = np.random.RandomState(999)\n",
        "indices_train = rng.permutation(len(X_train))\n",
        "indices_test = rng.permutation(len(X_test))\n",
        "\n",
        "# Use the shuffled indices to shuffle the data\n",
        "X_train = X_train.iloc[indices_train]\n",
        "y_train = y_train.iloc[indices_train]\n",
        "X_test = X_test.iloc[indices_test]\n",
        "y_test = y_test.iloc[indices_test]\n",
        "\n",
        "# Minmax scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the Random Forest classifier\n",
        "RF = RandomForestClassifier(n_estimators=100, random_state=999)\n",
        "\n",
        "# Train the Random Forest classifier\n",
        "RF.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = RF.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_gaU_IRmwUtT",
        "outputId": "3c2edbc7-b521-4b20-a755-6f41c1fb7c23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[110  65]\n",
            " [ 45 180]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.63      0.67       175\n",
            "           1       0.73      0.80      0.77       225\n",
            "\n",
            "    accuracy                           0.73       400\n",
            "   macro avg       0.72      0.71      0.72       400\n",
            "weighted avg       0.72      0.72      0.72       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RAkE8GqxxAXy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}